24-08-2012, 10:05 PM
i need Personalized Ontology Model for Web[/size][/font] Information Gathering Project source code
24-08-2012, 10:05 PM
i need Personalized Ontology Model for Web[/size][/font] Information Gathering Project source code
11-03-2014, 07:55 PM
i need personalized ontology source code
12-03-2014, 10:14 AM
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22-05-2014, 11:18 AM
A Personalized Ontology Model for Web Information Gathering
A Personalized Ontology .pdf (Size: 1.51 MB / Downloads: 21) Abstract As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful. INTRODUCTION The amount of web-based informa- tion available has increased dramatically. How to gather useful information from the web has become a challenging issue for users. Current web information gathering systems attempt to satisfy user requirements by capturing their information needs. For this purpose, user profiles are created for user background knowledge description [12], [22], [23]. User profiles represent the concept models possessed by users when gathering web information. A concept model is implicitly possessed by users and is generated from their background knowledge. While this concept model cannot be proven in laboratories, many web ontologists have observed it in user behavior [23]. When users read through a document, they can easily determine whether or not it is of their interest or relevance to them, a judgment that arises from their implicit concept models. If a user’s concept model can be simulated, then a superior representation of user profiles can be built. To simulate user concept models, ontologies—a knowl- edge description and formalization model—are utilized in personalized web information gathering. Such ontologies are called ontological user profiles [12], [35] or personalized ontologies [39]. To represent user profiles, many researchers have attempted to discover user background knowledge through global or local analysis. RELATED WORK Ontology Learning Global knowledge bases were used by many existing models to learn ontologies for web information gathering. For example, Gauch et al. [12] and Sieg et al. [35] learned personalized ontologies from the Open Directory Project to specify users’ preferences and interests in web search. On the basis of the Dewey Decimal Classification, King et al. [18] developed IntelliOnto to improve performance in distributed web information retrieval. Wikipedia was used by Downey et al. [10] to help understand underlying user interests in queries. These works effectively discovered user background knowledge; however, their performance was limited by the quality of the global knowledge bases. Aiming at learning personalized ontologies, many works mined user background knowledge from user local informa- tion. Li and Zhong [23] used pattern recognition and association rule mining techniques to discover knowledge from user local documents for ontology construction. Tran et al. [42] translated keyword queries to Description Logics’ conjunctive queries and used ontologies to represent user background knowledge. Zhong [47] proposed a domain ontology learning approach that employed various data mining and natural-language understanding techniques. Navigli et al. [28] developed OntoLearn to discover semantic concepts and relations from web documents. Web content mining techniques were used by Jiang and Tan [16] to discover semantic knowledge from domain-specific text documents for ontology learning. Finally, Shehata et al. [34] captured user information needs at the sentence level rather than the document level, and represented user profiles by the Conceptual Ontological Graph. The use of data mining techniques in these models lead to more user background knowledge being discovered. However, the knowledge discovered in these works contained noise and uncertainties. |
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